Automated Detection of Isolated <scp>REM</scp> Sleep Behavior Disorder Using Computer Vision
This work improves prior art by using a 2D camera routinely used in sleep laboratories and improving performance by adding only 3 features. This approach could be implemented in clinical sleep laboratories to facilitate and improve the diagnosis of iRBD. Coupled with automated detection of REM sleep, it should also be tested in the home environment using conventional infrared cameras to detect and/or monitor RBD. ANN NEUROL 2025;97:860-872.
Authors
- George Adaimi (ORCID: https://orcid.org/0000-0003-1932-9431)
- Emmanuel During (ORCID: https://orcid.org/0000-0002-0539-5115)
- Alexandre Alahi (ORCID: https://orcid.org/0000-0002-5004-1498)
- Emmanuel Mignot (ORCID: https://orcid.org/0000-0002-6928-5310)
- Anahid Hekmat (ORCID: https://orcid.org/0000-0002-9468-9907)
- Oliver Sum‐Ping (ORCID: https://orcid.org/0000-0001-7414-8583)
- Niraj Gupta
- Ankit Parekh (ORCID: https://orcid.org/0000-0002-5396-0553)
- Joanna Galati
- Li Zhou (ORCID: https://orcid.org/0000-0002-2998-0300)
- Salonee Marwaha
- Mohamed Abdelfattah (ORCID: https://orcid.org/0009-0002-5732-4340)
Institutions
- Palo Alto University (US)
- Chinese University of Hong Kong (HK)
- École Polytechnique Fédérale de Lausanne (CH)
- Stanford University (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Annals of Neurology
- Published
- 2025-01-09
- DOI
- https://doi.org/10.1002/ana.27170
- Citations
- 12
- Primary Topic
- Obstructive Sleep Apnea Research
- Type
- article
- Field-Weighted Citation Impact
- 13.97